# Run the Pipeline Container (locally)

## This section will teach you:

* How to run a container locally (for debugging!)
* How to run your container on AWS Batch (for fun!)
* Watch Disdat re-use prior results, and learn how to force it to recompute tasks. 

## Local container execution in your local context

The Disdat CLI provides a `run` command that almost exactly mirrors `apply`.  

```bash
$dsdt run . pipelines.dependent_tasks.B
```

{% hint style="warning" %}
Note: `dsdt run` requires the directory in which your `setup.py` exists.    

`dsdt run <directory of setup.py> <pipeline> <optional pipeline args>`
{% endhint %}

By default the run command:

* Runs your container via the local Docker client
* Runs in your current context (placing output bundles in that context)
* The current implementation waits till the container exits to return the result

{% hint style="warning" %}
Did your output contain:

```bash
===== Luigi Execution Summary =====

Scheduled 1 tasks of which:
* 1 complete ones were encountered:
    - 1 DriverTask(...)

Did not run any tasks
```

That's weird!   It didn't seem to run our tasks (**A** or **B**).  That's because Disdat noticed you might have already run this job and found the *prior result bundle.*  &#x20;
{% endhint %}

{% hint style="info" %}
Pro Tip:  If you want to see what you're container is doing, you can follow along with this command in another terminal:

```bash
$docker logs -f <container name>
```

{% endhint %}

## Forcing Disdat to recompute tasks

Sometimes you want to re-compute a particular task or a task and all its upstream dependencies.  To do so you can use `-f` to recompute the last task or `--force-all` to recompute the whole pipeline (careful with that).  Those flags apply to both `run` and to `apply`

```bash
$dsdt run -f . pipelines.dependent_tasks.B
```

That will ensure we make another version of the output bundle.&#x20;